AbstractDistinctive features define a multidimensional structure that must be implemented in speech production and perception. A multilevel Gaussian General Recognition Theory model is presented as a model of multidimensional feature perception. The model is fit to data from three experiments probing identification of noise-masked, naturally-produced labial and alveolar English stop consonants [p], [b], [t], and [d] in onset (syllable-initial) and coda (syllable-final) position. The results indicate systematic perceptual deviations from simple place and voicing structure in individual subjects and at the group level. Comparing onset and coda positions shows that syllable position modulates the deviation patterns, and comparing speech-shaped noise and multi-talker babble indicates that deviations from simple feature structure are reasonably robust to variation in noise characteristics. Possible causes of the observed perceptual confusion patterns are discussed, and extensions of this work to studies of feature structure in speech production and investigation of non-native speech perception are briefly outlined.